Sentences with phrase «reduces uncertainties in climate models»

Not exact matches

Reducing uncertainties in the models could lead to better long - term assessments of climate, Esposito says.

By improving the understanding of how much radiation CO2 absorbs, uncertainties in modelling climate change will be reduced and more accurate predictions can be made about how much Earth is likely to warm over the next few decades.

«A cloud system - resolved model can reduce one of the greatest uncertainties in climate models, by improving the way we treat clouds,» Wehner said.

A new integrated climate model developed by Oak Ridge National Laboratory and other institutions is designed to reduce uncertainties in future climate predictions as it bridges Earth systems with energy and economic models and large - scale human impact data.

A new integrated computational climate model developed to reduce uncertainties in future climate predictions marks the first successful attempt to bridge Earth systems with energy and economic models and large - scale human impact data.

PNNL researchers play a key role in reducing uncertainty through improved process understanding and modeling of climate processes such as clouds and aerosols.

Understanding how well climate models represent these processes will help reduce uncertainties in the model projections of the effects of global warming on the world's water cycle.

If we can get climate models to more credibly simulate current cloud patterns and observed cloud changes, this might reduce the uncertainty in future projections

The work of Schmittner et al. demonstrates that climates of the past can provide potentially powerful information to reduce uncertainty in future climate predictions and evaluate the likelihood of climate change that is larger than captured in present models.

I was wondering for some time now, how much the findings of the work of scientists, be it the IPCC, be it the PIK in Potsdam or what have you, can be taken for granted in order for policy makers to make valuable decisions (e.g. cutting carbon emissions by half by 2050) and if the uncertainties in the models might outweigh certain decisions to reduce carbon emissions so that in the end it might happen that these uncertainties make these decisions obsolete, because they do not suffice to avoid «dangerous climate change»?

To reduce uncertainties in climate - change projections, it is essential to prioritize the improvement of the most important uncertain physical processes in climate models.

Improving the scientific understanding of all climate feedbacks is critical to reducing the uncertainty in modeling the consequences of doubling the CO2 - equivalent concentration.

Over decades, improvements in observations of the present climate, reconstructions of ancient climate, and computer models that simulate past, current, and future climate have reduced some of the uncertainty in forecasting how rising temperatures will ripple through the climate system.

Innovative new approaches to climate data analysis, continued improvements in climate modeling, and instigation and maintenance of reference quality observation networks such as the U.S. Climate Reference Network (http://www.ncdc.noaa.gov/crn/) all have the potential to reduce uncertaclimate data analysis, continued improvements in climate modeling, and instigation and maintenance of reference quality observation networks such as the U.S. Climate Reference Network (http://www.ncdc.noaa.gov/crn/) all have the potential to reduce uncertaclimate modeling, and instigation and maintenance of reference quality observation networks such as the U.S. Climate Reference Network (http://www.ncdc.noaa.gov/crn/) all have the potential to reduce uncertaClimate Reference Network (http://www.ncdc.noaa.gov/crn/) all have the potential to reduce uncertainties.

In fairness, there remains considerable uncertainty in aerosol effects, but if there will be real progress in narrowing the credible range for climate sensitivity, it has to come from reducing the still too wide uncertainty in aerosol effects, not from flogging climate models which assume aerosol offsets inconsistent with the best available measured effectIn fairness, there remains considerable uncertainty in aerosol effects, but if there will be real progress in narrowing the credible range for climate sensitivity, it has to come from reducing the still too wide uncertainty in aerosol effects, not from flogging climate models which assume aerosol offsets inconsistent with the best available measured effectin aerosol effects, but if there will be real progress in narrowing the credible range for climate sensitivity, it has to come from reducing the still too wide uncertainty in aerosol effects, not from flogging climate models which assume aerosol offsets inconsistent with the best available measured effectin narrowing the credible range for climate sensitivity, it has to come from reducing the still too wide uncertainty in aerosol effects, not from flogging climate models which assume aerosol offsets inconsistent with the best available measured effectin aerosol effects, not from flogging climate models which assume aerosol offsets inconsistent with the best available measured effects.

The Process Study and Model Improvement (PSMI) Panel's mission is to reduce uncertainties in the general circulation models used for climate variability prediction and climate change projections through an improved understanding and representation of the physical processes governing climate and its variation.

Knowing that the spread in ECS is mostly related to uncertainties in low - cloud feedback, it seems obvious that constraining how low clouds respond to global warming can reduce the spread of climate sensitivity among models.

«Reducing the wide range of uncertainty inherent in current model predictions of global climate change will require major advances in understanding and modeling of both (1) the factors that determine atmospheric concentrations of greenhouse gases and aerosols, and (2) the so - called «feedbacks» that determine the sensitivity of the climate system to a prescribed increase in greenhouse gases.»

And beyond the post-facto model evaluation, it will be interesting to see whether new climate models will take advantage of emergent constraints to improve their simulation of present - day climate and to reduce uncertainties in future projections.

In the 1990's, a growing sense of the infeasibility of reducing uncertainties in global climate modeling emerged in response to the continued emergence of unforeseen complexities and sources of uncertaintieIn the 1990's, a growing sense of the infeasibility of reducing uncertainties in global climate modeling emerged in response to the continued emergence of unforeseen complexities and sources of uncertaintiein global climate modeling emerged in response to the continued emergence of unforeseen complexities and sources of uncertaintiein response to the continued emergence of unforeseen complexities and sources of uncertainties.

A key problem for reducing the uncertainty in climate projections is historical records of change are often too short to test the skill of climate models, raising concerns over our ability to successfully plan for the future.

The data generated in this laboratory is used to reduce the uncertainty associated with representing the organic aerosol lifecycle in climate models.

In the early 1990's there was the belief in the feasibility of reducing uncertainties in climate science and climate models, and a consensus seeking approach was formalized by the IPCIn the early 1990's there was the belief in the feasibility of reducing uncertainties in climate science and climate models, and a consensus seeking approach was formalized by the IPCin the feasibility of reducing uncertainties in climate science and climate models, and a consensus seeking approach was formalized by the IPCin climate science and climate models, and a consensus seeking approach was formalized by the IPCC.

Thus, using various kinds of climate model ensembles including both MMEs and SMEs, we may expect to reduce uncertainties in climate prediction in the future.

The DOE support includes funding from the Regional and Global Climate Modeling programme to the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Scientific Focus Area, from the Terrestrial Ecosystem Sciences programme to the Next Generation Ecosystem Experiments — Tropics, and from the Early Career programme (DE-SC0012152).

Zhang, M., S. Klein, D. Randall, R. Cederwall, and A. Del Genio, 2005: Introduction to special section on «Toward Reducing Cloud - Climate Uncertainties in Atmospheric General Circulation Models».

Further research emphasis is needed in these areas if we are to reduce uncertainty in modelled forecasts of the ecological consequences of climate change.

Two other important records from satellite instruments — one from MODIS and the other from MISR — don't agree well over land, so scientists hope that data from other other sensors like SeaWiFS might help resolve some of the discrepancies and reduce the overall uncertainty about aerosols in climate models.